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This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

Blockchain technology, renowned for its decentralization, security, reliability, and data integrity, has the potential to revolutionize businesses globally. However, its full potential remains unrealized due to adoption barriers, necessitating further studies to address these challenges. Identifying these barriers is crucial for businesses and practitioners to effectively tackle them. This systematic review analyzed 70 eligible studies out of 1944 gathered from various databases to understand and identify common blockchain adoption barriers. The Technology–Organization–Environment (TOE) framework was the most popular theory used in these studies. Despite differences in variable definitions, financial constraints, lack of stakeholder collaboration and coordination, and social influences like resistance to change and negative perceptions emerged as the top three barriers. The supply chain domain had the highest number of studies on blockchain adoption. Notably, there was a significant increase in studies addressing blockchain adoption in 2023, comprising 34.2% of the total reviewed studies. This review provides a comprehensive overview of identified barriers, serving as a valuable foundation for future research. Understanding these challenges allows researchers to design targeted studies aimed at developing solutions, strategies, and innovations to overcome obstacles hindering blockchain adoption.

This study investigated the prevalence of food color utilization in 20,382 pre-packaged foods available for sale in Hong Kong in 2019.

Ingredient lists from the 2019 cross-sectional FoodSwitch Hong Kong database were screened to identify the presence of 35 common food colors, based on their name or respective E-number. Descriptive statistics were computed for the prevalence (%) and the number of food colors (total, natural and synthetic) used.

Food colors were found in 19.8% of the audited sample. Natural variants were more prevalent than synthetic ones (17.2 vs 3.9%). The majority (89.5%) of colored foods used one to two types, though some included more than four types of food colors. Notably, E160 (carotenoids) appeared most frequently (8.4% of all foods; 42.4% of colored foods), followed by E150 (caramel; 7.4 and 37.4%, respectively) and E102 (tartrazine; 2.1 and 10.8%, respectively). Regional disparities were observed, with Asian products more likely to incorporate at least one food color.

This audit suggests that one in five pre-packaged foods in Hong Kong contains food colors, emphasizing the need for updated risk assessments due to their widespread use in the food supply.

This study aims to identify the factors and strategies that motivate Thai construction professionals to adopt Blockchain Technology (BT). Previous research highlights BT’s characteristics but lacks focus on the features most persuasive for Thai construction professionals.

Using Q methodology with 28 participants from the academic, construction and advanced technology sectors, this study explores their perceptions on BT adoption and addresses the gap in identifying persuasive features for Thai construction professionals.

The analysis identified eight distinct professional groups, each with unique perceptions of BT’s motivating factors. Based on these insights, seven strategies were proposed to promote BT adoption. A key finding is that BT adoption is influenced not only by professionals’ roles, positions and accountability but also by their existing technological competencies.

The use of Q methodology, while insightful, may not capture the full complexity of attitudes toward BT adoption. Additionally, the focus on the Thai construction industry and the small sample size may limit its generalizability to other cultural and economic contexts.

Identifying professional categories based on BT preferences and implementing strategies – such as automated systems, smart contracts, education centers and pilot projects – can enhance productivity in the Thai construction industry, drawing on global practices to address local challenges.

– With Thailand’s extensive infrastructure projects supporting ASEAN’s transportation hub vision, integrating blockchain is expected to enhance productivity and project outcomes, contributing to Thailand’s national infrastructure development goals.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

The construction industry faces public criticism for issues like wastefulness, inefficiency, slim profits, scheduling setbacks, budget overruns, quality concerns, trust deficits, transparency, coordination, communication and fraud. This paper aims to assess the nexus between barriers and drivers for adopting blockchain in construction and its impact on construction lifecycle.

A quantitative research approach was used to collect data using a well-structured questionnaire survey. The survey, which used snowball sampling, included 155 Nigerian construction experts that included architects, builders, quantity surveyors and engineers in the built environment. The data were analysed using partial least squares structural equation modelling (PLS-SEM), which allowed for a thorough evaluation of the proposed relationships as well as industry-specific insights.

The study's findings validate the conceptual framework established. The results indicate that implementing blockchain across all stages of construction projects has the potential to improve the construction process by 88.2% through its drivers. However, there were no significant relationships found between the barriers to adopting blockchain and the potential application areas in the construction lifecycle.

This research was carried out in the South-western which is one of the six geo-political zones/regions in Nigeria, using a cross-sectional survey method. The study did not investigate the interdependence of the identified categories of drivers and barriers, limiting a comprehensive understanding of the complex dynamics and interactions influencing blockchain adoption in construction. The study is expected to stimulate further exploration and generate new insights on how blockchain technology (BT) can influence various stages of the construction lifecycle.

The findings will be immensely beneficial to both professionals and practitioners in the Nigerian construction industry in learning about the potential of BT application in improving the construction lifecycle.

This paper developed and assessed a conceptual framework by investigating the interrelationships between the constructs. The findings have important implications for the construction industry, as they offer opportunities to improve the construction process and overall lifecycle. The findings are useful for researchers interested in the potential impact of BT on the construction lifecycle and its wider implications.

The smart contract provides an opportunity to improve existing contract management practices in the construction projects by replacing traditional contracts. However, translating the contracts into computer languages is considered a major challenge which has not been investigated. Thus, it is necessary to: (1) identify the obstructing clauses in real-world contracts; and (2) analyze the replacement's technical and economic feasibility. This paper aims to discuss the aforementioned objectives.

This study identified the flexibility clauses of traditional contracts and their corresponding functions through inductive content analysis with representative standard contracts as materials. Through a speculative analysis in accordance to design science paradigm and new institutional economics, the economic and technical feasibility of existing approaches, including enumeration method, fuzzy algorithm, rough sets theory, machine learning and artificial intelligence, to transform respective clauses (functions) into executable codes are analyzed.

The clauses of semantic flexibility and structural flexibility are identified from the contracts. The transformation of semantic flexibility is economically and/or technically infeasible with existing methods and materials. But with more data as materials and methods of rough sets or machine learning, the transformation can be feasible. The transformation of structural flexibility is technically possible however economically unacceptable.

Given smart contracts' inability to provide the required flexibility for construction projects, smart contracts will be more effective in less relational contracts. For construction contracts, the combination of smart contracts and traditional contracts is recommended. In the long run, with the sharing or trading of data in the industry level and the integration of machine learning or artificial intelligence reducing relevant costs, the automation of contract management can be achieved.

This study contributes to the understanding of the smart contract's limitations in industry scenarios and its role in construction project management.

This study aims to apply the concept of attention allocation and integrates the theory of planned behavior to shed light on the role of tourism online attention and transportation infrastructure in the causal relationship between the digital economy and tourism growth.

This study hand-curated the tourism online attention and transportation infrastructure data of 341 prefecture-level cities in China from 2011 to 2019. A series of econometric models were then used to systematically analyze the mediating role of attention allocation between the digital economy and tourism growth, as well as the moderating role of transportation infrastructure.

The digital economy enhances tourist arrivals and destination revenue and holds true even after exogenous shocks, instrumental variables and a set of robustness tests. The digital economy boosts tourism prosperity by attracting online attention from tourists, and transportation infrastructure amplifies the positive impact of the digital economy on tourism growth. The effectiveness of digital economy-empowered tourism varies depending on factors such as tourism dependency, regional characteristics, tourism resource types and urban agglomerations.

This work provides new digital tourism insights from an intra-tourism perspective, revealing the inner mechanisms through which the digital economy influences tourism growth.

本研究整合注意力分配的概念与计划行为理论, 阐明了旅游网络注意力和交通基础设施在数字经济与旅游业增长的因果关系中的作用。

本研究手工整理了 2011–2019 年中国 341 个地级及以上城市的旅游网络注意力和交通基础设施数据, 运用一系列计量经济模型系统分析了注意力分配在数字经济与旅游业增长之间的中介作用, 以及交通基础设施的调节作用。

数字经济提高了游客数量和目的地收入, 即使在经过外生冲击、工具变量和一系列稳健性测试后, 数字经济仍然稳健。数字经济通过吸引游客的网络关注促进了旅游业的繁荣, 而交通基础设施增强了数字经济对旅游业增长的积极影响。数字经济赋能旅游业的效果因旅游依赖性、地区特征、旅游资源类型和城市群等因素而异。

本研究基于旅游业的内部视角为数字旅游提供了新的见解, 揭示了数字经济影响旅游业增长的内在机制。

este estudio aplica el concepto de asignación de atención e integra la teoría del comportamiento planificado para arrojar luz sobre el papel de la atención en línea del turismo y la infraestructura de transporte en la relación causal entre la economía digital y el crecimiento del turismo.

este estudio recopiló manualmente los datos de atención turística en línea y de infraestructura de transporte de 341 ciudades a nivel de prefectura en China entre 2011 y 2019. A continuación, se utilizó una serie de modelos econométricos para analizar sistemáticamente el papel mediador de la canalización de la atención entre la economía digital y el crecimiento del turismo, así como el papel moderador de la infraestructura de transporte.

la economía digital aumenta las llegadas de turistas y los ingresos de los destinos, y se mantiene incluso después de perturbaciones exógenas, variables instrumentales y un conjunto de pruebas de robustez. La economía digital impulsa la prosperidad del turismo al atraer la atención en línea de los turistas, y la infraestructura de transporte amplifica el impacto positivo de la economía digital en el crecimiento del turismo. La eficacia del turismo potenciado por la economía digital varía en función de factores como la dependencia del turismo, las características regionales, los tipos de recursos turísticos y las aglomeraciones urbanas.

Este trabajo ofrece nuevas perspectivas sobre el turismo digital desde una perspectiva intra-turística, revelando los mecanismos internos a través de los cuales la economía digital influye en el crecimiento del turismo.

Shifting to OLSL classes during the pandemic can bring learners ambivalent experiences: negative, positive or both appraisals toward the technologies. However, few studies have examined how ambivalent experiences can influence students' learning behaviors, specifically cyberslacking and active participation. Using the challenge-hindrance stressor framework, this study investigates the impact of challenge and hindrance appraisals on these learning behaviors.

This study uses a mixed methods approach to answer research questions. An interview was conducted to identify the key components of ambivalent appraisals, and a survey was conducted to empirically examine the impact of challenge and hindrance appraisals on learners' behaviors in online live streaming learning (OLSL) contexts. The data of 675 university students were analyzed using structural equation modeling.

This study found that hindrance appraisal leads to cyberslacking while challenge appraisal leads to active participation, but it can also cause cyberslacking. Social presence has a double-edged effect, acting as both a facilitator and inhibitor, strengthening the effect of hindrance appraisal on cyberslacking and the impact of challenge appraisal on active participation.

Prior studies have primarily focused on the negative side (techno-distress) of technology appraisals. This study simultaneously examines the positive side, techno-eustress, on learners' behaviors in OLSL contexts, and explores the moderating effects of social presence. This study contributes to the technostress and technology adaptation literature by revealing how technology-induced ambivalent appraisals impact behavioral responses. It offers important theoretical and practical implications for education tool designers.

This study aims to explore the use of modified graphene (MG) in copper wire drawing lubricants to enhance their friction-reducing and anti-wear capabilities.

Graphene was modified using oleic and stearic acids to improve its dispersibility in lubricants. Various concentrations of MG were then introduced into a copper wire drawing lubricant to investigate their tribological performance. Wear mechanisms were evaluated with scanning electron microscopy, optical microscopy, Raman spectroscopy and energy dispersive spectroscopy (EDS).

The best concentration of MG is 1.5 Wt.%, at which the copper wire drawing oil exhibits a friction coefficient and wear rate of 0.085 and 2.11 × 10⁻⁶ mm³/Nm, respectively, representing decreases of 22.7% and 47.6% compared to the base oil. It was further found that the addition of 1.5 Wt.% MG to a copper wire drawing fluid with a water content of 70% resulted in a 30.3% reduction in friction coefficient compared to the base oil. Raman spectroscopy and EDS analysis confirmed that the MG tribo-film formed on the worn copper disc effectively minimized friction and wear.

This study analyzes the tribological performance of different concentrations of MG in copper wire drawing oils, establishing a basis for the application of MG in copper wire drawing fluids.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0399/